CS/EE 217 GPU Architecture and Parallel Programming - Winter 2019

Course Information


Welcome to CS/EE 217!

Class Syllabus

Class webpage and communication

The class webpage is located at http://danielwong.org/teaching/csee217/winter19

Information, resources, and announcements related to the class will be posted to the webpage.

In addition, we will be using iLearn for assignments, and Piazza for discussions and help.

You will need an ENGR account. EE and CEN students should already have one. If you do not, you can create one here: https://www.engr.ucr.edu/secured/systems/login.php

Course Description

Introduces the popular CUDA based parallel programming environments based on Nvidia GPUs. Covers the basic CUDA memory/threading models. Also covers the common data-parallel programming patterns needed to develop a high-performance parallel computing applications. Examines computational thinking; a broader range of parallel execution models; and parallel programming principles.

Prerequisite: CS160 Concurrent Programming and Parallel Systems Strong C/C++ programming skills are required


Grade Breakdown

Letter Grade Percentage
A > 93%
A- > 90%
B+ > 87%
B > 83%
B- > 80%
C+ > 77%
C > 73%
C- > 70%

Lab Policies


Academic Integrity

Here at UCR we are committed to upholding and promoting the values of the Tartan Soul: Integrity, Accountability, Excellence, and Respect. As a student in this class, it is your responsibility to act in accordance with these values by completing all assignments in the manner described, and by informing the instructor of suspected acts of academic misconduct by your peers. By doing so, you will not only affirm your own integrity, but also the integrity of the intellectual work of this University, and the degree which it represents. Should you choose to commit academic misconduct in this class, you will be held accountable according to the policies set forth by the University, and will incur appropriate consequences both in this class and from Student Conduct and Academic Integrity Programs. For more information regarding University policy and its enforcement, please visit: http://conduct.ucr.edu.


You are expected to attend all lectures. While the slides contain all the information you need to know, some of the contents don't make sense unless you attend lecture. =)

Tentative Schedule

The following schedule is tentative and is subject to change.

Note: You need to be registered in Piazza to access the lecture slides.

Week Date Topic Assignments Slides
1 January 7, M Introduction, OS/Architecture Review Introduction.pptx
1 January 9, W CUDA C CudaC.pptx
1 January 11, F CUDA Parallelism Lab 0 - CUDA Setup CudaParallelism.pptx
2 January 14, M TB / Warp Scheduling GPU-Architecture.pptx
2 January 16, W GPGPU-Sim Simulator Lab 0 - GPGPU-Sim Setup
2 January 18, F Reduction Algorithm / Control Divergence Lab 1 - Reduction, Lab 0 Due Reduction.pptx
3 January 21, M No class - MLK Day
3 January 23, W Matrix Multiply MatrixMultiply.pptx
3 January 25, F Matrix Multiply (cont.) Lab 2 - Tiled Matrix Multiplication
4 January 28, M DRAM / Memory Coalescing Lab 1 Due
4 January 30, W GPGPU Memory Optimizations
4 February 1, F Stencil/Convolution
5 February 4, M Review Lab 2 Due
5 February 6, W Midterm Exam
5 February 8, F Histogram & Atomic Operations Lab 3 - Histogram,
Final Project Assigned
6 February 11, M High-level Libraries (cuBLAS, cuDNN, TensorFlow, NCCL)
6 February 13, W High-level Libraries
6 February 15, F High-level Libraries Lab 4 - cuDNN
7 February 18, M No class - Presidents Day Lab 3 Due
7 February 20, W Project progress meeting
7 February 22, F Project progress meeting
8 February 25, M Modern CUDA
8 February 27, W Modern CUDA Lab 4 Due
8 March 1, F Modern CUDA Extra Credit Lab - Streams
9 March 4, M GPU Architecture
9 March 6, W GPU Architecture
9 March 8, F GPU Architecture
10 March 11, M GPU Research Trends E.C. Lab Due
10 March 13, W Review
10 March 15, F Final Exam
11 March 20, W Final Project Due